[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …
reliability, selection, and dynamic response of the power system is essential. Governments …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Multi-agent deep reinforcement learning for HVAC control in commercial buildings
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
[HTML][HTML] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning
Dynamic energy dispatch is an integral part of the operation optimization of integrated
energy systems (IESs). Most existing dynamic dispatch schemes depend heavily on explicit …
energy systems (IESs). Most existing dynamic dispatch schemes depend heavily on explicit …
Deep reinforcement learning for continuous electric vehicles charging control with dynamic user behaviors
This paper aims to crack the individual EV charging scheduling problem considering the
dynamic user behaviors and the electricity price. The uncertainty of the EV charging demand …
dynamic user behaviors and the electricity price. The uncertainty of the EV charging demand …
Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources
S Lee, DH Choi - IEEE Transactions on Industrial Informatics, 2020 - ieeexplore.ieee.org
This article proposesa novel federated reinforcement learning (FRL) approach for the
energy management of multiple smart homes with home appliances, a solar photovoltaic …
energy management of multiple smart homes with home appliances, a solar photovoltaic …
[HTML][HTML] Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning …
With rising energy costs and concerns about environmental sustainability, there is a growing
need to deploy Home Energy Management Systems (HEMS) that can efficiently manage …
need to deploy Home Energy Management Systems (HEMS) that can efficiently manage …
[HTML][HTML] Deep reinforcement learning for home energy management system control
The use of machine learning techniques has been proven to be a viable solution for smart
home energy management. These techniques autonomously control heating and domestic …
home energy management. These techniques autonomously control heating and domestic …